In passive seismic monitoring, seismic energy is generated by microseismic events resulting typically from shear-stress release on pre-existing geological structures (e.g., faults and fractures), for example due to production/injection induced local earth stress condition perturbations. The seismic energy can be recorded using sensors such as geophones or seismometers placed in a monitoring well and/or on the surface of the earth.
Microseismic events may be seen as small earthquakes. Hydraulic fracturing induced microseismic events typically have a Richter magnitude ML<−1. Production induced events typically have a Richter magnitude ML<3. Such seismic events are commonly recorded by receivers at distances of over 1 km from the origin of the events. This poses a challenge for event detection due to low signal-to-noise ratios.
Known methods for automated microseismic event detection include short-time-average/long-time-average (STA/LTA) detectors and correlation-type detectors. The STA/LTA detector calculates the energy ratio of short-time window to long-time window and declares the appearance of seismic events when the ratio exceeds a threshold. The correlation detector screens seismic events by calculating a correlation coefficient between the received signal and a template event known as the master event, assuming events that are to be detected have similar waveforms as the master event.
Simple STA/LTA detectors are broadly applicable, but have high false alarm rates when an aggressive threshold is set to detect smaller signals. Correlation detectors are highly sensitive, having high detection probability at low false alarm rates. However, they are applicable only to strictly repetitive sources confined to very compact source regions.
Event detection in a signal subspace has been suggested. (See, for example, D. B. Harris (2006): “Subspace detectors: Theory,” Lawrence Livermore Natl. Lab. Rep. UCRL-TR-222758, 46 pp., Lawrence Livermore Natl. Lab., Livermore, Calif.) In these proposed methods, a single matching template in a correlation detection method is replaced with a suite (subspace) of basis vectors that are combined linearly to match occurrences of variable signals from a specific source region.